Length: 3 Days
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Decision Analysis Training  Workshop

Decision Analysis (DA) is a form of decision-making that involves identifying and assessing all aspects of a decision, and taking actions based on the decision that produces the most favorable outcome.

The goal of decision analysis is to ensure that decisions are made with all the relevant information and options available. As a form of decision-making, the fundamentals of decision analysis can be used to solve a multitude of problems, from complex business issues to simple everyday problems.

Corporations are especially focused on the principles of DA. This approach is often used to evaluate and model the potential outcomes of various decisions to determine the correct course of action. To be effective, the business needs to understand multiple aspects of a problem to result in a well-informed decision.

The analysis entails understanding various goals, outcomes, and uncertainties involved, including the use of probabilities to measure the expected outcome of various decisions.

Complexity in the modern world, along with information quantity, uncertainty, and risk, make it necessary to provide a rational decision making framework.

Of course, a decision needs a decision maker who is responsible for making decisions. This decision maker has a number of alternatives and must choose one of them. The objective of the decision-maker is to choose the best alternative.

When this decision has been made, events that the decision-maker has no control over may have occurred. Each combination of alternatives, followed by an event happening, leads to an outcome with some measurable value.

A key component to decision analysis (DA) methodology is the expected value.

Expected value is a commonly used financial concept. In finance, it indicates the anticipated value of an investment in the future. Expected value (also known as EV, expectation, average, or mean value) is a long-run average value of random variables.

It also indicates the probability-weighted average of all possible values. By determining the probabilities of possible scenarios, one can determine the EV of the scenarios. The concept is frequently used with multivariate models and scenario analysis. It is directly related to the concept of expected return.

After a model is constructed, it is important to find the expected value (EV) to evaluate which decision results in the most favorable outcome.

Decision Analysis Training Workshop Course by Tonex

Decision Analysis Training Workshop, Introduction Decision Analysis is a 3-day training workshop designed for professionals in engineering, business, innovative technology, defense and aerospace, medicine and other fields. Decision analysis is a powerful way to think through and analyze decision problems involving uncertainty, complexity and time.

Participants will learn about decision analysis and how it can help when it comes to a tough decision by structuring the problem in terms of alternatives, information and preferences. Uncertainties and tradeoffs are made explicitly and allows decision makers to clarify their personal preferences with greater confidence.

Participants will learn the skills needed to participate in the application of Decision
Analysis to projects and programs.

In Class & Live Online Training

  • 2-day instructor led training course
  • Additional One-on-one support after the course up to 6 months

Decisions are choices between alternative courses of action:

  • Involves managing uncertain outcomes
  • Involves tradeoffs between different benefits

Learning Objectives

After completing this course, the participants will be able to:

  • Describe the decision-making environments of certainty and uncertainty.
  • Define parameters for your decision analysis task, project or program
  • Step through the life of a decision analysis process
  • Construct decision tree diagrams.
  • Construct both a payoff table and an opportunity-loss table.
  • Apply root cause analysis and cause and effect principles (5-why’s, Fishbone/Ishikawa diagrams)
  • Identify key principles of network analysis, modeling and simulation (Monte Carlo)
  • Define the expected value criterion using forecasting techniques.
  • Apply the expected value criterion in decision situations.
  • Compute the cost of uncertainty and value of perfect information.
  • Develop a decision tree and explain how it can aid decision making in an uncertain environment.

course details

Decision Analysis 101

  • What is Decision Analysis?
  • Applying Decision Analysis
  • Why are Decisions Difficult?
  • Consequences, Uncertainty, and Ambiguity
  • A Scalable Process: Uncertainty and Ambiguity
  • Real World Decisions
  • The Role of Decision Analysis
  • Decision Analysis Process
  • Decision Making in a Complex Scenarios
  • Differentiation Between Ambiguity and Uncertainty
  • Engagement in a Project or Strategy

Framing Decision Problems and Scenarios

  • Modeling Preferences and Decision Analysis Phases
  • Measuring Uncertainty
  • Decision Strategies and Confidence Through Clarity
  • Decision Management
  • Interpretation to Gain Insight and Agreement
  • Tools and Techniques
  • Decision Trees
  • Group Decision Making
  • Root-Cause Analysis
  • Risk Analysis
  • Program and Project Evaluation
  • Conflict Analysis
  • Rapid Analysis
  • Benchmarking
  • Judgement of Decision Quality and Effectiveness

Uncertainty and Making Choices

  • Decisions and Uncertainty
  • Measures of Merit
  • Time Value of Money
  • Dealing with Risk
  • The Certain Equivalent
  • Principles of Evaluations
  • Using Distinctions
  • Defining Possibilities

Making Compelling Decisions

  • The Decision Elements
  • Why We Have Difficulty Achieving High-Quality Decisions
  • How Do You Achieve Decision Quality?
  • The Ten Principles Of Good Decision-Making
  • How Do You Measure Decision Quality?
  • The Scalable Decision Process (SDP)
  • Structuring Phase
  • Evaluation Phase
  • Agreement Phase

Creating a Shared Understanding of the Problem

  • Framing the Problem
  • The Participants in the Process
  • Developing an Appropriate Frame
  • Creating Alternatives
  • Preparing for Evaluation

Developing a Decision Model

  • Building Influence Diagrams
  • Decision Trees
  • Computer Modeling Programs
  • What is Probability?
  • Probability Basics
  • Venn Diagrams
  • States of Information
  • Probability Trees
  • Reversing the Tree
  • Using and Understanding Distributions

Using Simulation to Solve Decision Problems

  • What is a Monte Carlo Simulation?
  • Why Use Monte Carlo Simulation?
  • Using Random Numbers to Simulate Reality
  • Using the Results of a Monte Carlo
  • Commercial Software
  • The Role of Monte Carlo
  • Working with a Real Monte Carlo Simulation (Using Anaconda python toolbox)

Using Uncertain Information and Judgment

  • Using Limited Information
  • Gathering Information
  • Uncovering and Dealing with Biases
  • Assessing Information
  • Using Probability as the Language of Uncertainty
  • Discretizing the Information
  • Gaining Insight Through Evaluation
  • Getting to Agreement
  • Using the Scalable Decision Process on Large Projects
  • Portfolio Analysis and Management

Workshop 1

  • Implementing the Decision Analysis Process
  • Implementing Decision Analysis
  • What is Right for Your Organization?
  • Implementation Issues
  • Real World Problems
  • Implications and Reactions
  • Decision Response Inventory Exercise (DRIVE)
  • Facilitation and Analysis Summaries
  • Eliciting Issues
  • Decision Hierarchy
  • Influence Diagrams
  • Strategy Table
  • Assessment
  • Decision Trees
  • Decision Quality Radar Chart

Supplementary Course Book:

Introduction to Decision Analysis, 3rd Edition

A Practitioner’s Guide to Improving Decision Quality by David C. Skinner, Edited by Paul Wicker

Introduction to Decision Analysis by David Skinner

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